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New free trial of Python-friendly SAS Event Stream Processing adds drag-and-drop

Started ‎03-10-2020 by
Modified ‎03-10-2020 by
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Analytics helps us make better decisions and streaming analytics helps us make better decisions faster. Any time you are dealing with scenarios where data is coming to you fast and furious like IoT, app logs, web traffic, social media, well almost anything, streaming analytics can add real value. Given that and the fact that we just launched the new version of the SAS Event Stream Processing (ESP as we call it) free trial, it shouldn’t require any analytics to make a quick decision to give it a try.


This article provides a quick overview of the 30-day free trial of SAS Event Stream Processing. Highlights include drag-and-drop interfaces for Studio and Streamviewer.


SAS ESP is a Streaming Analytics platform that can analyze fast-moving data (up to millions of events / second), detecting patterns of interest as they occur and thus supporting real-time decision making.


How to start your free trial of SAS Event Stream Processing


For starters, it’s easy to get your trial. You start at and hit the “Get free trial” button, logon with a SAS profile or create one and in a couple of minutes you should be ready to go. You should be receiving two emails, the second one has a link for a tenant created on our SAS Analytics Cloud just for YOU!


Once you log in, you will see a main SAS Event Stream Processing tile under the “Apps” menu option on the left:






Three dots = three supporting tasks


This right here is a pretty useful tile as the three dots on the right have links to the following supporting tasks:


  • More info: Jumps to a help page starting of with an overview but allowing access to help content relevant to all the rich functionality of ESP
  • Support: As the name suggests, takes you to the support page with access to tutorials, training, and other product support information
  • Restart: Allows user to restart their environment if needed








Monitor data usage and manage teams

SAS ESP Data menu.png


The “Data” menu option on the left provides visibility to data usage for the tenant – each starts with 75 GB of storage allowing you to upload your own data as you begin to experiment.


The “Team” option is for managing the team of folks whom you may want to participate in this trial along with you. You can invite up to four more people. Each team gets a tenant shared by the users in the team. 


Three interface options


SAS ESP interfaces.PNG




















Clicking on the main tile starts the journey and shows you three options to choose from:


  1. The JupyterLab environment is targeted towards a data scientist persona who likes to code in Python. This is based on ESPPy, an open source package for Python to design, test and deploy streaming analytics examples. This environment uses Plotly for visualization.
  2. SAS ESP Studio environment is the drag-and-drop based user interface that can be used by users who like to work with visual environment for building out streaming analytics examples.
  3. ESP Streamviewer is also drag-and-drop based user interface that is used to design and visualize dashboards as you begin to test.

For each of these environments, we’ve included pre-built scenarios. They represent some basic concepts of building out “streaming analytics projects” and a sprinkling of some simple use cases across different industry verticals. Below is a sneak peek into some use case examples:


ESPPy (Python interface) based


Smart Infrastructure

Anomaly detection in Floodlights in a parking lot based on energy consumption

Connected Vehicle

Geo-fencing based real-time alerts for a connected car

Image Analytics

Real-time detection of object of interest in an image


Sentiment analysis on Product reviews


ESP Studio (UI drag-and-drop) based


Smart Infrastructure

Anomaly detection in Floodlights in a parking lot based on energy consumption


Minimizing false positives


Using Vibration data to identify emerging issues with a rotary motor


Identify shelf inventory conditions such as stock-outs using Computer Vision


Anomaly detection on a smart grid


You can use these examples just to get familiar with the type of use cases streaming analytics can be applied to or enhance/edit as needed to make them your own – all the necessary ingredients like the datasets, the analytics files are accessible from the trial environment.


The idea behind each of these examples is to present a common use case and illustrate the power of streaming analytics to shift the decision making towards “real-time.”


Now it’s over to you to explore where you can apply faster decisioning in your organizations! Get your 30-day trial of SAS Event Stream Processing.

Start My Free Trial

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‎03-10-2020 02:15 PM
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